Vehicle braking assembly

ABSTRACT

A system including a supply valve disposed between a chamber and a pressure source, a discharge valve disposed between the chamber and an external atmosphere, a first control unit, and a second control unit. The first control unit coupled with the supply valve by a first switch and with the discharge valve by a second switch. The first control unit outputting signals to the first and second switches to control the supply and discharge valves. The second control unit coupled with the discharge valve by a third switch and a fourth switch, the second control unit outputting signals to the third and fourth switches to control the supply and discharge valves. The first control unit may include a first microcontroller to control the signals of the first control unit using an artificial intelligence (AI) neural network having artificial neurons arranged in layers and connected with each other by connections.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 16/725,326, filed on 23 Dec. 2019, which is acontinuation-in-part of U.S. patent application Ser. No. 15/524,053,filed on 3 May 2017, which is a national stage application, filed under35 U.S.C. § 371, of International Patent Application No.PCT/IB2015/058730, filed on 12 Nov. 2015, which claims priority toItalian Patent Application No. TO2014A000945, filed on 13 Nov. 2014. Theentire disclosures of these applications are incorporated herein byreference.

BACKGROUND Technical Field

The subject matter described herein relates to braking systems ofvehicles.

Discussion of Art

Existing braking systems for railway vehicles may compriseelectro-pneumatic assemblies controlled by electronic units of themicroprocessor type. The design of these braking systems may be governedby specific standards (in Europe, for example, the EN 50126 standardrelating to system definition, the EN 50128 standard concerning softwaredesign and development, and the EN 50129 standard relating to hardwarespecifications and design). These standards introduced the concept of“Safety Integrity Level” (SIL hereafter) which defines the degree ofreduction of risk to human safety that can be associated with a givenfunction relating to a braking installation.

A braking installation for railway vehicles may be designed to execute aplurality of functions, for example (but not only) service braking,parking braking, safety braking, emergency braking, braking correctionin case of wheel sliding or locking (wheel slide protection), andholding braking.

A different SIL level may be required for each of these functions: inparticular, the emergency braking and safety braking functions must beimplemented with safety levels in the range from SIL=3 to SIL=4, withreference to a scale running from a minimum of SIL=0 to a maximum ofSIL=4.

In the present state of the art, purely mechanical-pneumatic solutionsmay be used in virtually all cases to execute the emergency braking andsafety braking functions, since these solutions enable the requisite SILlevels to be reached and verified in a convenient manner.

FIG. 1 of the attached drawings shows, by way of example, anelectro-pneumatic braking installation for vehicles in which the safetybraking pressure is determined by a valve 1, commonly known as an LPPV(Load Proportional Pressure Valve). This valve may be used to generate abraking pressure proportional to the detected weight of the railwayvehicle (or of a part thereof, for example a bogie), to provide thegreatest possible deceleration within the limits of wheel-to-railadhesion defined at the design stage. The valve, various implementationsof which are known, may execute a transfer function of the type shown inqualitative terms in FIG. 2 , where the pressure Pi at the input of thevalve 1 is shown on the horizontal axis, and the pressure Po at theoutput of this valve is shown on the vertical axis. According to FIG. 2, when the pressure Pi varies between a value Ptare and a maximum valuePimax, the output pressure Po may vary between a minimum value Pomin anda maximum value Pomax, along a straight line characterized by a slopeangle α. Additionally, when the pressure Pi varies between Ptare and 0,the output pressure Po may vary between the value Pomin and anintermediate value P*o, according to a straight line characterized by aslope angle β. The pressure P*o is such that the vehicle is alwaysbraked if a fault occurs in the suspension, such that an excessively lowpressure value is caused, as shown in the broken-line continuation ofthe straight line having the slope α.

With reference to FIG. 1 again, the pressure Po at the output of thevalve 1 is sent (for example) to the control chamber of a relay valve 2,through one or more solenoid safety valves 3. These solenoid valves 3are normally in the state of pneumatic conduction when de-energized, andare energized by a safety loop of the braking system. Safety braking isapplied by de-energizing the safety loop, the pressure Po from theoutput of the valve 1 then being propagated by the control chamber ofthe relay valve 2, which amplifies its power, at its output 2 a, towardsthe brake cylinder or cylinders (not shown).

The known solution described above is one of various possible solutionsused to execute a braking function with a safety level equal to orgreater than the SIL 3 level defined in the EN 50126 standard.

Although these solutions are satisfactory in terms of the safety level,they have considerable drawbacks due to the complexity and nature of thedevices and components used, such as springs, rubber diaphragms, sealingrings, and the like. The use of these components has a negative effecton the accuracy of the functional characteristics provided, and on theirrepeatability when the operating temperature varies, in view offunctional requirements which commonly specify operating temperatureranges from −40° C. to +70° C. Additionally, the provision of operatingcharacteristics such as those shown in FIG. 2 by purelymechanical-pneumatic means requires complicated solutions, such asspecific ratios between the rubber diaphragm surfaces and the springloading, these ratios determining the slope angle α, β and the points ofintersection of the straight lines with this Cartesian axes.

Also, with the known solutions of the purely mechanical-pneumatic type,it is substantially impossible to calibrate the operatingcharacteristics on board a vehicle during the normal adjustment of thevehicle (during commissioning), and therefore, if the slopes α, β or thepressure values at the points of intersection of the straight lines withthe Cartesian axes have to be varied, the ratios between the surfaces ofthe rubber diaphragms and the spring loadings must be completelyre-planned, which will obviously create delays in the adjustment of thevehicle.

Furthermore, the variation of the aforesaid functional characteristicsdue to the tolerances of the materials and the fluctuations caused bytemperature variations and ageing results in a considerable lack ofprecision in the stopping distances of railway vehicles during emergencyand/or safety braking.

It is also known that the use of microprocessor systems for the feedbackcontrol of pneumatic solenoid valves enables the characteristic functionof the valve 1 described above to be reproduced conveniently, whileproviding much greater accuracy than that allowed by existingmechanical-pneumatic components, over a range of temperature and timevariations, thus making the aforesaid stopping distances much moreprecise and repeatable. Moreover, certain characteristics such as theslopes α and β can be easily and rapidly modified simply by usingsoftware methods to reprogram parameters.

FIG. 3 of the appended drawings shows an embodiment of anelectro-pneumatic assembly 10 for controlling the pneumatic pressure ina chamber or volume 11, such as the volume of a brake cylinder, or thecontrol chamber of a relay valve which controls the supply of pressureto the volume of a brake cylinder. This assembly 10 comprises a solenoidsupply or filling valve 12 adapted to connect the chamber 11 selectivelyto a pressure source PS or to the atmosphere, and a vent or dischargevalve 13 adapted to allow or selectively prevent the connection of thechamber 11 to the atmosphere. The solenoid valves 12 and 13 are providedwith respective control solenoids 12 a, 13 a to which respectiveelectronic switches are coupled in the manner described below.

The chamber or volume 11 is connected to a conduit 14 which connects theoutput of the solenoid valve 12 to the input of the solenoid valve 13.

When the solenoids 12 a and 13 a of the solenoid valves 12 and 13 arede-energized, these solenoid valves appear in the condition shown inFIG. 3 : the volume or chamber 11 is connected to the atmosphere, andthe pressure within it is reduced to the value of atmospheric pressure.

When the solenoid valves 12 and 13 are both energized, the first valvesupplies the chamber 11 with a flow of air taken from the pressuresource, while the second valve disconnects the chamber 11 from theatmosphere. Thus the pressure in the chamber 11 is increased.

When the solenoid valve 12 is de-energized and the solenoid valve 13 isenergized, the chamber 11 is disconnected both from the pressure sourceand from the atmosphere, and the pressure within it remainssubstantially unchanged.

The behavior of the electro-pneumatic assembly 10 of FIG. 3 with thevariation of the conditions of energizing and de-energizing of thesolenoids 12 a and 13 a is summarized in Table 1 below.

TABLE 1 12a 13a Pressure in 11 0 0 DECREASE 0 1 MAINTENANCE 1 1 INCREASE1 0 — 0 = de-energized 1 = energized — = condition not used

By suitably modulating the energizing conditions or states of thesolenoid valves 12 and 13 shown in Table 1, it is possible to produceand maintain in the volume or chamber 11 any value of pressure betweenthe pressure PS of the source and atmospheric pressure Patm.

FIGS. 4 and 5 show variant embodiments of the electro-pneumatic assembly10. In these figures, parts and elements identical or corresponding tothose described previously have been given the same reference numeralsas those used previously.

The mode of operation of the electro-pneumatic assemblies 10 of FIGS. 4and 5 can be summarized as shown in Tables 2 and 3 below.

TABLE 2 12a 13a Pressure in 11 0 0 MAINTENANCE 0 1 DECREASE 1 0 INCREASE1 1 —

TABLE 3 12a 13a Pressure in 11 0 0 INCREASE 1 0 MAINTENANCE 1 1 DECREASE0 1 —

Once again, in the case of the electro-pneumatic assemblies 10 of FIGS.4 and 5 , by suitably modulating the energizing conditions or states ofthe solenoid valves 12 and 13 it is possible to produce and maintain inthe volume or chamber 11 any value of pressure between PS and PATM.

FIG. 6 shows, in the form of a block diagram, an electronic controlsystem 15 according to the prior art, for controlling anelectro-pneumatic assembly according to one of FIGS. 3 to 5 . Thissystem 15 essentially comprises a processing and control unit 16, of themicroprocessor or microcontroller type, which receives at an input asignal L containing information on the weight of the vehicle (or of asingle bogie of the vehicle), for example the instantaneous value of thepressure Pi shown on the horizontal axis of FIG. 2 .

At another input, the unit 16 receives a signal P representing thepneumatic pressure within the volume or chamber 11, detected by means ofa suitable sensor. The unit 16 may receive further signals or input dataII, which are not essential for the purposes of the present description.

By means of bias circuits 17 and 18, the unit 16 controls correspondingsolid-state electronic switches 19 and 20, such as p-channel MOStransistors or simple NPN transistors, which control theenergizing/de-energizing condition of the solenoids 12 a and 13 arespectively, in parallel with which respective recirculation diodes 21and 22 may be connected. In the control system 15 of FIG. 6 , theelectronic switches 19 and 20 are connected in series with the windings12 a and 13 a, between a d.c. power source Vcc and the earth GND.

The unit 16 may if necessary supply further output signals OO, relatingto other processes not essential for the purposes of the presentdescription.

By implementing suitable closed-loop control algorithms, for example PIDalgorithms, “fuzzy” algorithms, or algorithms of the on-off type withhysteresis (also known as “bangbang” control algorithms), the unit 16can be designed to provide the characteristic shown in the diagram ofFIG. 2 , in such a way that the pressure in the container or volume 11corresponds to the pressure Po in this diagram. For this purpose, theunit 16 receives, through an input port, the values of a set ofparameters PP which characterize the control algorithm. The values ofthese parameters are stored in a non-volatile memory of the unit 16.

As an alternative to the implementation shown schematically in FIG. 6 ,the solenoids 12 a and 13 a may be connected to the earth GND, while theassociated switches 19 and 20 may be connected to the d.c. power source.In this case, the switches 19 and 20 can be n-channel MOS transistors orPNP transistors.

In view of the EN 50126, EN 50128 and EN 50129 standards, if thefunction implemented by the unit 16, for example the pressurecharacteristic according to the diagram of FIG. 2 , requires a safetylevel equivalent to SIL 3 or SIL 4, then, since the unit 16 is the onlydevice contributing to the execution of this safety function, thecorresponding software must also be implemented with a process having asafety level of SIL 3 or SIL 4, as specified, in particular, in the EN50128 standard. However, this software implementation process ischaracterized by extremely high organizational, financial andmaintenance-related costs, which frequently make its use less attractiveby comparison with the more conventional mechanical-pneumatic systems,even though these suffer from all the aforementioned drawbacks.

BRIEF DESCRIPTION

In one embodiment, an assembly includes a supply valve configured to bedisposed between a chamber and a pressure source, a discharge valvedisposed between the chamber and an external atmosphere, and a firstcontrol unit coupled with the supply valve by a first switch and withthe discharge valve by a second switch. The first control unit isconfigured to output signals to the first switch and the second switchto control the supply valve and the discharge valve. The assembly alsoincludes a second control unit coupled with the discharge valve by athird switch and a fourth switch. The second control unit is configuredto output signals to the third switch and the fourth switch to controlthe supply valve and the discharge valve. The first control unit mayinclude a first microcontroller to control the signals of the firstcontrol unit using an artificial intelligence (AI) neural network havingartificial neurons arranged in layers and connected with each other byconnections. The first microcontroller may receive feedback based on thesignals that are selected by the artificial neurons and may train the AIneural network by changing one or more of the connections between theartificial neurons in the AI neural network based on the feedback thatis received.

In one embodiment, an assembly includes a first control unit coupledwith a supply valve by a first switch and with a discharge valve by asecond switch. The first control unit is configured to output signals tothe first switch and the second switch to control the supply valve andthe discharge valve to control a pressure inside a chamber. The assemblyalso includes a second control unit coupled with the discharge valve bya third switch and a fourth switch. The second control unit isconfigured to output signals to the third switch and the fourth switchto control the supply valve and the discharge valve. The first controlunit and the second control unit are configured to output the signalssuch that the supply valve and the discharge valve open or close basedon whether the signals from the first control unit match or conflictwith the signals from the second control unit. The first control unitmay include a first microcontroller to control the signals of the firstcontrol unit using an artificial intelligence (AI) neural network havingartificial neurons arranged in layers and connected with each other byconnections. The first microcontroller may receive feedback based on thesignals that are selected by the artificial neurons and may train the AIneural network by changing one or more of the connections between theartificial neurons in the AI neural network based on the feedback thatis received.

In one embodiment, an assembly includes a supply valve and a dischargevalve coupled in series with each other between a pressure source and anexternal atmosphere. The supply valve and the discharge valve areconfigured to be coupled with a chamber that is pressurized by thepressure source. The assembly also includes a first control unit coupledwith the supply valve by a first switch and with the discharge valve bya second switch. The first control unit is configured to output signalsto the first switch and the second switch to control the supply valveand the discharge valve. The assembly also includes a second controlunit coupled with the discharge valve by a third switch and a fourthswitch. The second control unit is configured to output signals to thethird switch and the fourth switch to control the supply valve and thedischarge valve. The first control unit may include a firstmicrocontroller to control the signals of the first control unit usingan artificial intelligence (AI) neural network having artificial neuronsarranged in layers and connected with each other by connections. Thefirst microcontroller may receive feedback based on the signals that areselected by the artificial neurons and may train the AI neural networkby changing one or more of the connections between the artificialneurons in the AI neural network based on the feedback that is received.

BRIEF DESCRIPTION OF THE DRAWINGS

The inventive subject matter may be understood from reading thefollowing description of non-limiting embodiments, with reference to theattached drawings, wherein below:

FIG. 1 , described above, is a diagram of a braking system for vehicles;

FIG. 2 , also described above, shows a characteristic of a valve forcontrolling the pressure proportionally to the load;

FIGS. 3 to 5 show three different embodiments of a pneumatic part of anelectro-pneumatic braking assembly according to one embodiment of theinventive subject matter;

FIG. 6 is a block diagram of a control system for an electro-pneumaticassembly;

FIGS. 7 to 12 are circuit diagrams, partially in block form, showingvarious embodiments of a control system for an electro-pneumaticassembly according to one embodiment of the inventive subject matter;and

FIG. 13 illustrates a functional block diagram of an example neuralnetwork that ca be used by a braking system for vehicles, according toone example.

DETAILED DESCRIPTION

FIG. 7 illustrates one example of a brake control system. This controlsystem can control operation of an electro-pneumatic assembly to executea pneumatic function on the basis of which a value of pneumatic pressureequal to or greater than a predetermined target, for example accordingto the characteristic shown in FIG. 2 , is produced in the volume orchamber 11 of the assembly according to FIG. 3 .

The control system includes two electronic processing and control units16 and 116, constructed for example in the form of microprocessor ormicrocontroller units, independent of one another. These units 16, 116are made, for example, in the form of physical devices which differ fromone another, and are designed to execute control strategies which areequivalent to one another, although they are implemented usingcorresponding software packages which are independent of, and generallydifferent from, one another.

The same input signals L, P and II as those defined above are suppliedto the units 16 and 116, together with respective data PP and PP′representing the values of parameters of the respective algorithmsimplemented in them. The units 16 and 116 also supply respective outputsignals OO and OO′.

The control unit 16 is designed to drive, through respective biascircuits 17 and 18, the electronic switches 19 and 20 which areessentially connected in series with the respective energizing solenoids12 a and 13 a of the solenoid valves 12 and 13. In turn, the electronicunit 116 has two outputs for driving, through bias circuits 117 and 118,corresponding electronic switches 119 and 120, connected, respectively,in parallel with the switches 19 and 20, between a ground reference(e.g., the earth GND or a vehicle chassis) and the energizing solenoids12 a and 13 a.

In the diagram according to FIG. 7 , the electronic switches 19, 20, 119and 120 are coupled to one another so as to form together an enablinglogic circuit designed to drive the solenoids 12 a and 13 a in such away that:

when the logic control signals sent to the switches by the control units16 and 116 are in agreement with one another, the energizing of thesolenoids 12 a and 13 a of the solenoid valves 12 and 13 enables thepressure in the volume or chamber 11 to be controlled in accordance withTable 1 above, in such a way that the pressure in this volume or chamber11 conforms (for example) to the characteristic shown in FIG. 2 ; and

conversely, when the logic control signals sent by the units 16 and 116towards the associated switches 19, 20 and 119, 120 conflict with oneanother, the logic control signals that are executed are those suppliedby the unit 16 or 116 which tends to produce the greater pressure insaid volume or chamber 11.

The solenoid 12 a (13 a) is energized according to a logical OR functionof the states of the switches 19 and 119 (20 and 120). With reference toTable 1, a conflict between the signals can occur if one of the twounits 16 and 116, using the associated electronic switches, tends to setthe condition of pressure decrease in the chamber or volume 11, whilethe other unit 116 or 16 tends to set the condition of pressuremaintenance. As a result of the OR connection between the switches 19and 119 and between the switches 20 and 120 respectively, the conditionof pressure maintenance will prevail. For example, the pressure will notincrease or decrease (e.g., by more than a threshold amount, such as10%).

Similarly, when one of the control units tends to set the condition ofpressure increase while the other control unit tends to set thecondition of maintenance, then, again as a result of the OR connectionbetween the switches 16 and 119 and between the switches 20 and 120respectively, the condition of pressure increase will prevail. Forexample, the pressure will increase (e.g., by at least a thresholdamount, such as 10%). Additionally, the condition of pressure increasewill also prevail over the condition of pressure decrease. For example,if one signal indicates a pressure increase while another signalindicates a pressure decrease, the pressure increase will occur.

Consequently, the system according to FIG. 7 can be used to execute apneumatic function adapted to produce, in the volume or chamber 11 ofFIG. 3 , a value of pressure equal to or greater than a predeterminedtarget value.

FIG. 8 shows the architecture of a control system for anelectro-pneumatic assembly. The diagram of FIG. 8 differs from that ofFIG. 7 in that the electronic switches 19 and 119 associated with thesolenoids 12 a of the solenoid valve 12 of FIG. 4 are connected inseries with one another, between this solenoid 12 a and the groundreference.

The valve arrangement according to FIG. 4 and the associated controlsystem according to FIG. 8 are such that, when the logic control signalssupplied by the control units 16 and 116 conflict with one another, thelogic control signals that are executed are those supplied by the unit16 or 116 which tends to produce the lower pressure in the volume orchamber 11 (of FIG. 4 ). This is due to the fact that the solenoid valvearrangement according to FIG. 4 corresponds to Table 2 shown above, andis also due to the logical AND connection of switches 19 and 119 and thelogical OR connection of switches 20 and 120.

Thus, with reference to Table 2, it can easily be seen that, if onecontrol unit 16 or 116 tends to set the condition of pressure decreasein the chamber or volume 11 while the other control unit 116 or 16 tendsto set the condition of pressure maintenance, then, as a result of theAND connection between the switches 19 and 119, the condition ofpressure decrease will prevail. Similarly, when one of the two units 16and 116 tends to set the condition of pressure increase while the otherunit 116 or 16 tends to set the condition of maintenance, then, as aresult of the logical OR connection between the switches 20 and 120 andthe logical AND connection between the switches 19 and 119, thecondition of pressure maintenance will prevail. Finally, the conditionof pressure maintenance, commanded by one of the two units, prevailsover the condition of pressure increase commanded by the other unit.Consequently, a pneumatic function is executed which is adapted toproduce a pressure equal to or less than a predetermined target value inthe volume or chamber 11.

The electro-pneumatic assembly can be used to obtain a pneumaticpressure equal to or greater than a predetermined target pressure in thevolume or chamber 11. In the diagram according to FIG. 9 , both theswitches 19 and 119 associated with the solenoid 12 a and the switches20 and 120 associated with the solenoid 13 a are coupled to one anotheraccording to a logical AND configuration.

The control system shown in FIG. 10 may differ from the system accordingto FIG. 9 in that the switches 19 and 119 are coupled to one anotheraccording to a logical OR configuration.

An electro-pneumatic assembly of this type can be used to execute apneumatic function adapted to produce a value of pressure equal to orgreater than a predetermined target value in the volume or chamber 11 ofFIG. 3 .

FIG. 11 shows a control system for executing a pneumatic functionadapted to produce a value of pressure according to a predeterminedtransfer function, according to the characteristic shown in FIG. 2 forexample, using a solenoid valve part which may conform to any one ofFIG. 3 , FIG. 4 , and FIG. 5 . The control system according to FIG. 11comprises, like the systems described above, two microprocessor ormicrocontroller control units, indicated by 16 and 116, which receivethe signals L, P, II and PP (PP′) described above. The unit 16 isdesigned to supply at its output two logical signals X1, X2, forcontrolling, respectively, the solenoids 12 and 13 a of the solenoidvalves 12 and 13. Similarly, the control unit 116 is designed to supplyat its output two logic control signals X11, X12, for controlling thesolenoids 12 and 13 a.

A further microprocessor or microcontroller control unit 1016 based onprogrammable logics such as FPGA logics also is provided. This unit 1016receives, as input, the same signals as those arriving at the units 16and 116, to which the control unit 1016 is connected by respectivetwo-way communication lines 23 and 123.

By executing closed-loop control algorithms such as PID algorithms,“fuzzy” algorithms, or algorithms of the on-off type with hysteresis,otherwise known as bang-bang control algorithms, the control units 16and 116 can produce, for example, the characteristic according to thediagram of FIG. 2 , where the value of the pressure on the horizontalaxis Pi is the pressure indicated by the load signals L, and thefeedback pressure for the control algorithm is represented by the signalP, corresponding to the pressure Po in the diagram of FIG. 2 .

As in the systems according to FIGS. 7 to 10 , the electronic units 16and 116 can execute different programs, derived from two differentalgorithms. The units 16 and 116 communicate with the unit 1016 throughthe lines 23 and 123. Such as by signals comprising communicationprotocols, or alternatively a set of hard-wired handshake signals.Through the lines 23 and 123, the units 16 and 116 communicaterespective auto-diagnosis signals to the unit 1016 which is designed toexecute diagnostic procedures to verify the correct operation of theunits 16 and 116, using a dedicated algorithm.

The unit 1016 controls the state of a switching device 30. This device30 can be constructed using electromechanical (relay) or solid-stateswitches, and has two outputs which, via drive circuits 31, 131, controlthe state of the solenoids 12 a and 13 a of the solenoid valves 12 and13.

The unit 1016 determines which of the two units 16 and 116 the directcontrol of the solenoids 12 a and 13 a is to be assigned to initially,by coupling the outputs of the switching device 30 selectively to theoutputs X1, X2 of the unit 16 or to the outputs X11 and X12 of thecontrol unit 116. The unit 1016 verifies that the selected control unitis correctly executing the predetermined pneumatic function, for examplethe function according to the characteristic shown in FIG. 2 .

The unit 1016 is also designed to periodically cause the switching ofthe switching device 30, assigning the control of the solenoids 12 a and13 a to one and the other of the units 16, 116 in alternate periods, toverify the availability of these units, that is to say to verify thatboth are capable of executing the control of said solenoids, in case oneof these two units proves to be longer capable of controlling saidsolenoids according to the pneumatic function to be executed.

FIG. 12 shows a further embodiment in which the electronic switches 19,20, 119 and 120 are connected to one another and to the solenoids 12 aand 13 a in the way shown in FIG. 7 . However, the embodiment accordingto FIG. 12 can be implemented not only with the configuration accordingto FIG. 7 , but with any one of the other configurations describedabove.

In the system according to FIG. 12 , the control units 16 and 116 aremonitored by respective monitoring and diagnostic devices 16M and 116M,constructed with the use of microcontrollers, for example.

When a monitoring device 16M or 116M detects an operating anomaly orfault in the associated unit 16 or 116, it disables the logic signalssent by the associated unit 16 or 116 to the corresponding switches 19,20 or 119, 120, for example by adjusting the associated bias circuits17, 18 or 117, 118.

In these embodiments, the units 16, 116, as well as the monitoring anddiagnostic devices 16M, 116M if necessary, can be integrated into asingle device, for example a dual core chip or FPGA device.

As previously stated, one or more of the braking systems describedherein may be implemented in an AI or machine-learning system. FIG. 13illustrates a functional block diagram of an example neural network 1302that can be used by a braking system. The braking system may reviewvarious inputs, described above, for example a signal L containinginformation on the weight of the vehicle, a signal P representing thepneumatic pressure within the volume or chamber, data PP and PP′representing values of parameters of respective algorithms, a signalcontaining information regarding an operational state of one or moresolenoids (e.g., energized, deenergized), a signal containinginformation regarding an operational state of the one or more switches,or the like. In an example, the neural network 1302 can represent a longshort-term memory (LSTM) neural network. In an example, the neuralnetwork 1302 can represent one or more recurrent neural networks (RNN).The neural network 1302 may be used to implement the machine learning asdescribed herein, and various implementations may use other types ofmachine learning networks. The neural network 1302 may include an inputlayer 1304, one or more intermediate or hidden layers 1308, and anoutput layer 1312. Each layer 1304, 1308, 1312 includes artificialindividual units, or neurons. Each neuron can receive information (e.g.,as input into the neural network 1302 or as received as output fromanother neuron in another layer or the same layer), process thisinformation to generate output, and provide the output to another neuronor as output of the neural network 1302. The input layer 1304 mayinclude several input neurons 1304 a, 1304 b . . . 1304 n. The hiddenlayer 1308 may include several intermediate neurons 1308 a, 1308 b . . .1308 n. The output layer 1312 may include several output neurons outputs1312 a, 1312 b . . . 1312 n. The inputs may include, for example,vehicle weight, pneumatic pressure, switch status, or the like.

Each neuron can receive an input from another neuron and output a valueto the corresponding output to another neuron (e.g., in the output layer1312 or another layer). For example, the intermediate neuron 1308 a canreceive an input from the input neuron 1304 a and output a value to theoutput neuron 1312 a. Each neuron may receive an output of a previousneuron as an input. For example, the intermediate neuron 1308 b mayreceive input from the input neuron 1304 b and the output neuron 1312 a.The outputs of the neurons may be fed forward to another neuron in thesame or different intermediate layer 1308.

The processing performed by the neurons may vary based on the neuron,but can include the application of the various rules or criteriadescribed herein to partially or entirely decide one or more aspects ofthe braking system, for example when to enable or release pressure inthe volume or chamber, when to energize or deenergize the solenoids,when to open or close the one or more switches or the like. The outputof the application of the rule or criteria can be passed to anotherneuron as input to that neuron. One or more neurons in the intermediateand/or output layers 1308, 1312 can determine matches between one ormore aspects of the braking system, for example that the weight of thevehicle requires a minimum pressure in the chamber. As used herein, a“match” may refer to a preferred operation of the braking system basedon the inputs, for example a preferred pressure in the chamber. Thepreferred operation may be based on increasing performance, efficiency,safety, longevity, or a combination of any or all of these factors. Thelast output neuron 1312 n in the output layer 1312 may output a match orno-match decision. For example, the output from the neural network 1302may be an that a solenoid needs to be energized for a given vehicleweight and chamber pressure level. Although the input layer 1304, theintermediate layer(s) 1008, and the output layer 1012 are depicted aseach including three artificial neurons, one or more of these layers maycontain more or fewer artificial neurons. The neurons can include orapply one or more adjustable parameters, weights, rules, criteria, orthe like, as described herein, to perform the processing by that neuron.

In various implementations, the layers of the neural network 1302 mayinclude the same number of artificial neurons as each of the otherlayers of the neural network 1302. For example, the vehicle weight,pneumatic pressure within the volume or chamber, or the like may beprocessed to provide information to the input neurons 1304 a-1304 n. Theoutput of the neural network 1302 may represent a match or no-match ofthe inputs to the a given output. More specifically, the inputs caninclude historical vehicle data. The historical vehicle data can beprovided to the neurons 1308 a-1308 n for analysis and matches betweenthe historical vehicle data. The neurons 1308 a-1308 n, upon findingmatches, may provide the potential connections as outputs to the outputlayer 1312, which can determine a connection, no connection, or aprobability of a connection.

In some embodiments, the neural network 1302 may be a convolutionalneural network. The convolutional neural network can include an inputlayer, one or more hidden or intermediate layers, and an output layer.In a convolutional neural network, however, the output layer may includeone fewer output neuron than the number of neurons in the intermediatelayer(s), and each neuron may be connected to each output neuron.Additionally, each input neuron in the input layer may be connected toeach neuron in the hidden or intermediate layer(s).

Such a neural network-based braking system can be trained by operators,automatically self-trained by the braking system itself, or can betrained both by operators and by the braking system itself to improvehow the system operates.

In one embodiment, an assembly may include a supply valve that can bedisposed between a chamber and a pressure source, a discharge valve thatmay be disposed between the chamber and an external atmosphere, and afirst control unit that can be coupled with the supply valve by a firstswitch and with the discharge valve by a second switch. The firstcontrol unit may output signals to the first switch and the secondswitch may control the supply valve and the discharge valve. Theassembly also may include a second control unit that can be coupled withthe discharge valve by a third switch and a fourth switch. The secondcontrol unit may output signals to the third switch and the fourthswitch to control the supply valve and the discharge valve. The firstcontrol unit may include a first microcontroller to control the signalsof the first control unit using an artificial intelligence (AI) neuralnetwork having artificial neurons arranged in layers and connected witheach other by connections. The first microcontroller may receivefeedback based on the signals that are selected by the artificialneurons and may train the AI neural network by changing one or more ofthe connections between the artificial neurons in the AI neural networkbased on the feedback that is received.

The supply valve and the discharge valve may increase a pressure in thechamber responsive to the signals from the first control unitconflicting with the signals from the second control unit. The supplyvalve and the discharge valve may decrease a pressure in the chamberresponsive to the signals from the first control unit conflicting withthe signals from the second control unit. The second control unit mayinclude a second microcontroller to control the signals of the secondcontrol unit using an AI neural network having artificial neuronsarranged in layers and connected with each other by connections. Thesecond microcontroller may receive feedback based on the signals thatare selected by the artificial neurons and may train the AI neuralnetwork by changing one or more of the connections between theartificial neurons in the AI neural network based on the feedback thatis received.

The supply valve and the discharge valve may increase a pressure in thechamber responsive to the signals from the first control unit matchingthe signals from the second control unit.

The supply valve and the discharge valve are configured to decrease apressure in the chamber responsive to the signals from the first controlunit matching the signals from the second control unit.

The supply valve and the discharge valve are coupled with each other ina series.

The supply valve and the discharge valve are configured to be coupledwith the chamber with the chamber between the supply valve and thedischarge valve.

In one embodiment, an assembly includes a first control unit coupledwith a supply valve by a first switch and with a discharge valve by asecond switch. The first control unit is configured to output signals tothe first switch and the second switch to control the supply valve andthe discharge valve to control a pressure inside a chamber. The assemblyalso includes a second control unit coupled with the discharge valve bya third switch and a fourth switch. The second control unit isconfigured to output signals to the third switch and the fourth switchto control the supply valve and the discharge valve. The first controlunit and the second control unit are configured to output the signalssuch that the supply valve and the discharge valve open or close basedon whether the signals from the first control unit match or conflictwith the signals from the second control unit. The first control unitmay include a first microcontroller to control the signals of the firstcontrol unit using an artificial intelligence (AI) neural network havingartificial neurons arranged in layers and connected with each other byconnections. The first microcontroller may receive feedback based on thesignals that are selected by the artificial neurons and may train the AIneural network by changing one or more of the connections between theartificial neurons in the AI neural network based on the feedback thatis received.

The first control unit and the second control unit are configured tocontrol the supply valve and the discharge valve to increase a pressurein the chamber responsive to the signals from the first control unitconflicting with the signals from the second control unit.

The first control unit and the second control unit are configured tocontrol the supply valve and the discharge valve to decrease a pressurein the chamber responsive to the signals from the first control unitconflicting with the signals from the second control unit.

The second control unit may include a second microcontroller to controlthe signals of the second control unit using an AI neural network havingartificial neurons arranged in layers and connected with each other byconnections. The second microcontroller may receive feedback based onthe signals that are selected by the artificial neurons and may trainthe AI neural network by changing one or more of the connections betweenthe artificial neurons in the AI neural network based on the feedbackthat is received.

The first control unit and the second control unit are configured tocontrol the supply valve and the discharge valve to increase a pressurein the chamber responsive to the signals from the first control unitmatching the signals from the second control unit.

The first control unit and the second control unit are configured tocontrol the supply valve and the discharge valve to decrease a pressurein the chamber responsive to the signals from the first control unitmatching the signals from the second control unit.

In one embodiment, an assembly includes a supply valve and a dischargevalve coupled in series with each other between a pressure source and anexternal atmosphere. The supply valve and the discharge valve areconfigured to be coupled with a chamber that is pressurized by thepressure source. The assembly also includes a first control unit coupledwith the supply valve by a first switch and with the discharge valve bya second switch. The first control unit is configured to output signalsto the first switch and the second switch to control the supply valveand the discharge valve. The assembly also includes a second controlunit coupled with the discharge valve by a third switch and a fourthswitch. The second control unit is configured to output signals to thethird switch and the fourth switch to control the supply valve and thedischarge valve. The first control unit may include a firstmicrocontroller to control the signals of the first control unit usingan artificial intelligence (AI) neural network having artificial neuronsarranged in layers and connected with each other by connections. Thefirst microcontroller may receive feedback based on the signals that areselected by the artificial neurons and may train the AI neural networkby changing one or more of the connections between the artificialneurons in the AI neural network based on the feedback that is received.

The supply valve and the discharge valve are configured to increase apressure in the chamber responsive to the signals from the first controlunit conflicting with the signals from the second control unit.

The supply valve and the discharge valve are configured to decrease apressure in the chamber responsive to the signals from the first controlunit conflicting with the signals from the second control unit.

The second control unit may include a second microcontroller to controlthe signals of the second control unit using an AI neural network havingartificial neurons arranged in layers and connected with each other byconnections. The second microcontroller may receive feedback based onthe signals that are selected by the artificial neurons and may trainthe AI neural network by changing one or more of the connections betweenthe artificial neurons in the AI neural network based on the feedbackthat is received.

The supply valve and the discharge valve are configured to increase apressure in the chamber responsive to the signals from the first controlunit matching the signals from the second control unit.

The supply valve and the discharge valve are configured to decrease apressure in the chamber responsive to the signals from the first controlunit matching the signals from the second control unit.

The supply valve and the discharge valve are coupled with each other andwith the chamber with the chamber disposed between the supply valve andthe discharge valve.

The supply valve may be a solenoid valve.

The discharge valve is another solenoid valve.

The singular forms “a”, “an”, and “the” include plural references unlessthe context clearly dictates otherwise. “Optional” or “optionally” meansthat the subsequently described event or circumstance may or may notoccur, and that the description may include instances where the eventoccurs and instances where it does not. Approximating language, as usedherein throughout the specification and claims, may be applied to modifyany quantitative representation that could permissibly vary withoutresulting in a change in the basic function to which it may be related.Accordingly, a value modified by a term or terms, such as “about,”“substantially,” and “approximately,” may not be limited to the precisevalue specified. In at least some instances, the approximating languagemay correspond to the precision of an instrument for measuring thevalue. Here and throughout the specification and claims, rangelimitations may be combined and/or interchanged, such ranges may beidentified and include all the sub-ranges contained therein unlesscontext or language indicates otherwise.

The above description is illustrative, and not restrictive. For example,the above-described embodiments (and/or aspects thereof) may be used incombination with each other. In addition, many modifications may be madeto adapt a particular situation or material to the teachings of theinventive subject matter without departing from its scope. While thedimensions and types of materials described herein define the parametersof the inventive subject matter, they are exemplary embodiments. Otherembodiments will be apparent to one of ordinary skill in the art uponreviewing the above description. The scope of the inventive subjectmatter should, therefore, be determined with reference to the appendedclaims, along with the full scope of equivalents to which such clausesare entitled.

While one or more embodiments are described in connection with a railvehicle system, not all embodiments are limited to rail vehicle systems.Unless expressly disclaimed or stated otherwise, the inventive subjectmatter described herein extends to multiple types of vehicle systems.These vehicle types may include automobiles, trucks (with or withouttrailers), buses, marine vessels, aircraft, mining vehicles,agricultural vehicles, or other off-highway vehicles. The vehiclesystems described herein (rail vehicle systems or other vehicle systemsthat do not travel on rails or tracks) can be formed from a singlevehicle or multiple vehicles. With respect to multi-vehicle systems, thevehicles can be mechanically coupled with each other (e.g., by couplers)or logically coupled but not mechanically coupled. For example, vehiclesmay be logically but not mechanically coupled when the separate vehiclescommunicate with each other to coordinate movements of the vehicles witheach other so that the vehicles travel together as a group. Vehiclegroups may be referred to as a convoy, consist, swarm, fleet, platoon,and train.

In one embodiment, the control unit may have a local data collectionsystem deployed that may use machine learning to enable derivation-basedlearning outcomes. The controller may learn from and make decisions on aset of data (including data provided by the various sensors), by makingdata-driven predictions and adapting according to the set of data. Inembodiments, machine learning may involve performing a plurality ofmachine learning tasks by machine learning systems, such as supervisedlearning, unsupervised learning, and reinforcement learning. Supervisedlearning may include presenting a set of example inputs and desiredoutputs to the machine learning systems. Unsupervised learning mayinclude the learning algorithm structuring its input by methods such aspattern detection and/or feature learning. Reinforcement learning mayinclude the machine learning systems performing in a dynamic environmentand then providing feedback about correct and incorrect decisions. Inexamples, machine learning may include a plurality of other tasks basedon an output of the machine learning system. In examples, the tasks maybe machine learning problems such as classification, regression,clustering, density estimation, dimensionality reduction, anomalydetection, and the like. In examples, machine learning may include aplurality of mathematical and statistical techniques. In examples, themany types of machine learning algorithms may include decision treebased learning, association rule learning, deep learning, artificialneural networks, genetic learning algorithms, inductive logicprogramming, support vector machines (SVMs), Bayesian network,reinforcement learning, representation learning, rule-based machinelearning, sparse dictionary learning, similarity and metric learning,learning classifier systems (LCS), logistic regression, random forest,K-Means, gradient boost, K-nearest neighbors (KNN), a priori algorithms,and the like. In embodiments, certain machine learning algorithms may beused (e.g., for solving both constrained and unconstrained optimizationproblems that may be based on natural selection). In an example, thealgorithm may be used to address problems of mixed integer programming,where some components restricted to being integer-valued. Algorithms andmachine learning techniques and systems may be used in computationalintelligence systems, computer vision, Natural Language Processing(NLP), recommender systems, reinforcement learning, building graphicalmodels, and the like. In an example, machine learning may be used forvehicle performance and behavior analytics, and the like.

In one embodiment, the control unit may include a policy engine that mayapply one or more policies. These policies may be based at least in parton characteristics of a given item of equipment or environment. Withrespect to control policies, a neural network can receive input of anumber of environmental and task-related parameters. These parametersmay include a vehicle weight, a pneumatic pressure, an identification ofa determined trip plan for a vehicle group, data from various sensors,and location and/or position data. The neural network can be trained togenerate an output based on these inputs, with the output representingan action or sequence of actions that the vehicle group should take toaccomplish the trip plan. During operation of one embodiment, adetermination can occur by processing the inputs through the parametersof the neural network to generate a value at the output node designatingthat action as the desired action. This action may translate into asignal that causes the braking system of the vehicle to operate. Thismay be accomplished via back-propagation, feed forward processes, closedloop feedback, or open loop feedback. Alternatively, rather than usingbackpropagation, the machine learning system of the controller may useevolution strategies techniques to tune various parameters of theartificial neural network. The controller may use neural networkarchitectures with functions that may not always be solvable usingbackpropagation, for example functions that are non-convex. In oneembodiment, the neural network has a set of parameters representingweights of its node connections. A number of copies of this network aregenerated and then different adjustments to the parameters are made, andsimulations are done. Once the output from the various models areobtained, they may be evaluated on their performance using a determinedsuccess metric. The best model is selected, and the vehicle controllerexecutes that plan to achieve the desired input data to mirror thepredicted best outcome scenario. Additionally, the success metric may bea combination of the optimized outcomes, which may be weighed relativeto each other.

The controller can use this artificial intelligence or machine learningto receive input (e.g., a location or change in location), use a modelthat associates locations with different operating modes to select anoperating mode of the one or more functional devices of the HOV unitand/or EOV unit, and then provide an output (e.g., the operating modeselected using the model). The controller may receive additional inputof the change in operating mode that was selected, such as analysis ofnoise or interference in communication signals (or a lack thereof),operator input, or the like, that indicates whether the machine-selectedoperating mode provided a desirable outcome or not. Based on thisadditional input, the controller can change the model, such as bychanging which operating mode would be selected when a similar oridentical location or change in location is received the next time oriteration. The controller can then use the changed or updated modelagain to select an operating mode, receive feedback on the selectedoperating mode, change or update the model again, etc., in additionaliterations to repeatedly improve or change the model using artificialintelligence or machine learning.

This written description uses examples to disclose the embodiments,including the best mode, and to enable a person of ordinary skill in theart to practice the embodiments, including making and using any devicesor systems and performing any incorporated methods. The claims definethe patentable scope of the disclosure, and include other examples thatoccur to those of ordinary skill in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal language of the claims.

What is claimed is:
 1. An assembly comprising: a supply valve configuredto be disposed between a chamber and a pressure source; a dischargevalve disposed between the chamber and an external atmosphere; a firstcontrol unit coupled with the supply valve by a first switch and withthe discharge valve by a second switch, the first control unitconfigured to output signals to the first switch and the second switchto control the supply valve and the discharge valve; and a secondcontrol unit coupled with the discharge valve by a third switch and afourth switch, the second control unit configured to output signals tothe third switch and the fourth switch to control the supply valve andthe discharge valve, wherein the first control unit includes a firstmicrocontroller configured to control the signals of the first controlunit using an artificial intelligence (AI) neural network havingartificial neurons arranged in layers and connected with each other byconnections, the first microcontroller configured to receive feedbackbased on the signals that are selected by the artificial neurons andtrain the AI neural network by changing one or more of the connectionsbetween the artificial neurons in the AI neural network based on thefeedback that is received.
 2. The assembly of claim 1, wherein thesecond control unit includes a second microcontroller configured tocontrol the signals of the second control unit using an AI neuralnetwork having artificial neurons arranged in layers and connected witheach other by connections, the second microcontroller configured toreceive feedback based on the signals that are selected by theartificial neurons and train the AI neural network by changing one ormore of the connections between the artificial neurons in the AI neuralnetwork based on the feedback that is received.
 3. The assembly of claim1, wherein the supply valve and the discharge valve are configured toincrease a pressure in the chamber responsive to the signals from thefirst control unit conflicting with the signals from the second controlunit.
 4. The assembly of claim 1, wherein the supply valve and thedischarge valve are configured to increase a pressure in the chamberresponsive to the signals from the first control unit matching thesignals from the second control unit.
 5. The assembly of claim 1,wherein the supply valve and the discharge valve are configured todecrease a pressure in the chamber responsive to the signals from thefirst control unit matching the signals from the second control unit. 6.The assembly of claim 1, wherein the supply valve and the dischargevalve are coupled with each other in a series.
 7. The assembly of claim6, wherein the supply valve and the discharge valve are configured to becoupled with the chamber with the chamber between the supply valve andthe discharge valve.
 8. An assembly comprising: a first control unitcoupled with a supply valve by a first switch and with a discharge valveby a second switch, the first control unit configured to output signalsto the first switch and the second switch to control the supply valveand the discharge valve to control a pressure inside a chamber; and asecond control unit coupled with the discharge valve by a third switchand a fourth switch, the second control unit configured to outputsignals to the third switch and the fourth switch to control the supplyvalve and the discharge valve, wherein the first control unit includes afirst microcontroller configured to control the output signals of thefirst control unit using an artificial intelligence (AI) neural networkhaving artificial neurons arranged in layers and connected with eachother by connections, the first microcontroller configured to receivefeedback based on the output signals that are selected by the artificialneurons and train the AI neural network by changing one or more of theconnections between the artificial neurons in the AI neural networkbased on the feedback that is received, wherein the first control unitand the second control unit are configured to output the signals suchthat the supply valve and the discharge valve open or close based onwhether the signals from the first control unit match or conflict withthe signals from the second control unit.
 9. The assembly of claim 8,wherein the second control unit includes a second microcontrollerconfigured to control the signals of the second control unit using an AIneural network having artificial neurons arranged in layers andconnected with each other by connections, the second microcontrollerconfigured to receive feedback based on the signals that are selected bythe artificial neurons and train the AI neural network by changing oneor more of the connections between the artificial neurons in the AIneural network based on the feedback that is received.
 10. The assemblyof claim 8, wherein the first control unit and the second control unitare configured to control the supply valve and the discharge valve toincrease a pressure in the chamber responsive to the signals from thefirst control unit conflicting with the signals from the second controlunit.
 11. The assembly of claim 8, wherein the first control unit andthe second control unit are configured to control the supply valve andthe discharge valve to increase a pressure in the chamber responsive tothe signals from the first control unit matching the signals from thesecond control unit.
 12. The assembly of claim 8, wherein the firstcontrol unit and the second control unit are configured to control thesupply valve and the discharge valve to decrease a pressure in thechamber responsive to the signals from the first control unit matchingthe signals from the second control unit.
 13. An assembly comprising: asupply valve and a discharge valve coupled in series with each otherbetween a pressure source and an external atmosphere, the supply valveand the discharge valve configured to be coupled with a chamber that ispressurized by the pressure source; a first control unit coupled withthe supply valve by a first switch and with the discharge valve by asecond switch, the first control unit configured to output signals tothe first switch and the second switch to control the supply valve andthe discharge valve; and a second control unit coupled with thedischarge valve by a third switch and a fourth switch, the secondcontrol unit configured to output signals to the third switch and thefourth switch to control the supply valve and the discharge valvewherein the first control unit includes a first microcontrollerconfigured to control the output signals of the first control unit usingan artificial intelligence (AI) neural network having artificial neuronsarranged in layers and connected with each other by connections, thefirst microcontroller configured to receive feedback based on the outputsignals that are selected by the artificial neurons and train the AIneural network by changing one or more of the connections between theartificial neurons in the AI neural network based on the feedback thatis received.
 14. The assembly of claim 13, wherein the second controlunit includes a second microcontroller configured to control the signalsof the second control unit using an AI neural network having artificialneurons arranged in layers and connected with each other by connections,the second microcontroller configured to receive feedback based on thesignals that are selected by the artificial neurons and train the AIneural netwok by changing one or more of the connections between theartificial neurons in the AI neural network based on the feedback thatis received.
 15. The assembly of claim 13, wherein the supply valve andthe discharge valve are configured to increase a pressure in the chamberresponsive to the signals from the first control unit conflicting withthe signals from the second control unit.
 16. The assembly of claim 13,wherein the supply valve and the discharge valve are configured toincrease a pressure in the chamber responsive to the signals from thefirst control unit matching the signals from the second control unit.17. The assembly of claim 13, wherein the supply valve and the dischargevalve are configured to decrease a pressure in the chamber responsive tothe signals from the first control unit matching the signals from thesecond control unit.
 18. The assembly of claim 13, wherein the supplyvalve and the discharge valve are coupled with each other and with thechamber with the chamber disposed between the supply valve and thedischarge valve.
 19. The assembly of claim 13, wherein the supply valveis a first solenoid valve.
 20. The assembly of claim 19, wherein thedischarge valve is a second solenoid valve.